论文标题
基于遮挡积累，在动态环境中的视觉探望的移动对象检测
Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation
论文作者
论文摘要
检测移动对象是处理动态环境的重要功能。大多数移动的对象检测算法都是为无深度的颜色图像而设计的。对于通常很容易获得实时RGB-D数据的机器人导航，深度信息的利用将对障碍识别有益。 在这里，我们提出了一种使用RGB-D图像的简单移动对象检测算法。所提出的算法不需要估计背景模型。取而代之的是，它使用一个遮挡模型，该模型使我们能够在与主导场景的移动对象相混淆的背景上估算摄像头姿势。所提出的算法允许分离移动对象检测和视觉探光（VO），以便在动态情况下使用任意强大的VO方法，结合了移动对象检测，而动态环境的其他VO算法是密不可分的。在本文中，我们将密集的视觉探光仪（DVO）用作具有双平方回归权重的VO方法。实验结果表明，在情况下DVO的分割精度和性能提高。我们在公共数据集和数据集中验证我们的算法，这些算法也可以公开访问。
Detection of moving objects is an essential capability in dealing with dynamic environments. Most moving object detection algorithms have been designed for color images without depth. For robotic navigation where real-time RGB-D data is often readily available, utilization of the depth information would be beneficial for obstacle recognition. Here, we propose a simple moving object detection algorithm that uses RGB-D images. The proposed algorithm does not require estimating a background model. Instead, it uses an occlusion model which enables us to estimate the camera pose on a background confused with moving objects that dominate the scene. The proposed algorithm allows to separate the moving object detection and visual odometry (VO) so that an arbitrary robust VO method can be employed in a dynamic situation with a combination of moving object detection, whereas other VO algorithms for a dynamic environment are inseparable. In this paper, we use dense visual odometry (DVO) as a VO method with a bi-square regression weight. Experimental results show the segmentation accuracy and the performance improvement of DVO in the situations. We validate our algorithm in public datasets and our dataset which also publicly accessible.
